Capacity Predictor: A Machine Learning Approach to Ratings

Recorded On: 09/18/2020


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Traditionally, capacity formulas have trouble conveying uncertainty in the estimate as well as incorporating conflicting wealth indicators into a coherent ratings framework. To resolve both of these issues, we use publicly available survey data from the Federal Reserve to create a machine learning model of capacity as a function of identified assets. This framework can be utilized by prospect researchers in the form of an app, or it can be used to screen your database. The presentation will cover the traditional way of rating prospects; thinking about ratings as probabilities; an exploratory data analysis of the Federal Reserve's survey data; what went into creating our machine learning rating system; and a demonstration of the rating app.

This webinar is worth (1) CFRE point.

This session is part of the PD 2020: Conference Highlights Bundle.

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Capacity Predictor: A Machine Learning Approach to Ratings
Recorded 09/18/2020
Recorded 09/18/2020 Traditionally, capacity formulas have trouble conveying uncertainty in the estimate as well as incorporating conflicting wealth indicators into a coherent ratings framework. To resolve both of these issues, we use publicly available survey data from the Federal Reserve to create a machine learning model of capacity as a function of identified assets. This framework can be utilized by prospect researchers in the form of an app, or it can be used to screen your database. The presentation will cover the traditional way of rating prospects; thinking about ratings as probabilities; an exploratory data analysis of the Federal Reserve's survey data; what went into creating our machine learning rating system; and a demonstration of the rating app.

David Schemitsch

Data Science Engineer

Columbia University

Adam Bradford

Director, Prospect Development

Columbia University